In order to facilitate software interaction and increase user satisfaction, various research efforts have tackled the problem of software customization by modeling the user’s goals, skills, and preferences. In this thesis, we focus on run-time solutions for adapting various interface and interaction aspects of software. From an intelligent agent’s perspective, the system views this customization problem as a decision-theoretic planning problem under uncertainty about the user. We propose a methodological framework for developing intelligent software interaction and assistance. This framework has been instantiated in various case studies which are reviewed in the thesis. Through efforts of data collection experiments to learn model parameters, simulation experiments to assess system feasibility and adaptivity, and usability testing to assess user receptiveness, our case studies show that our approach can effectively carry out customizations according to different user preferences and adapt to changing preferences over time.

In order to facilitate software interaction and increase user satisfaction, various research efforts have tackled the problem of software customization by modeling the user’s goals, skills, and preferences. In this thesis, we focus on run-time solutions for adapting various interface and interaction aspects of software. From an intelligent agent’s perspective, the system views this customization problem as a decision-theoretic planning problem under uncertainty about the user. We propose a methodological framework for developing intelligent software interaction and assistance. This framework has been instantiated in various case studies which are reviewed in the thesis. Through efforts of data collection experiments to learn model parameters, simulation experiments to assess system feasibility and adaptivity, and usability testing to assess user receptiveness, our case studies show that our approach can effectively carry out customizations according to different user preferences and adapt to changing preferences over time.

abstract: Modern software and hardware systems are composed of a large number of components. Often different components of a system interact with each other in unforeseen and undesired ways to cause failures. Covering arrays are a useful mathematical tool for testing all possible t-way interactions among the components of a system. The two major issues concerning covering arrays are explicit construction of a covering array, and exact or approximate determination of the covering array number---the minimum size of a covering array. Although these problems have been investigated extensively for the last couple of decades, in this thesis we present significant improvements on both of these questions using tools from the probabilistic method and randomized algorithms. First, a series of improvements is developed on the previously known upper bounds on covering array numbers. An estimate for the discrete Stein-Lovász-Johnson bound is derived and the Stein- Lovász -Johnson bound is improved upon using an alteration strategy. Then group actions on the set of symbols are explored to establish two asymptotic upper bounds on covering array numbers that are tighter than any of the presently known bounds. Second, an algorithmic paradigm, called the two-stage framework, is introduced for covering array construction. A number of concrete algorithms from this framework are analyzed, and it is shown that they outperform current methods in the range of parameter values that are of practical relevance. In some cases, a reduction in the number of tests by more than 50% is achieved. Third, the Lovász local lemma is applied on covering perfect hash families to obtain an upper bound on covering array numbers that is tightest of all known bounds. This bound leads to a Moser-Tardos type algorithm that employs linear algebraic computation over finite fields to construct covering arrays. In some cases, this algorithm outperforms currently used methods by more than an 80% margin. Finally, partial covering arrays are introduced to investigate a few practically relevant relaxations of the covering requirement. Using probabilistic methods, bounds are obtained on partial covering arrays that are significantly smaller than for covering arrays. Also, randomized algorithms are provided that construct such arrays in expected polynomial time. / Dissertation/Thesis / Doctoral Dissertation Computer Science 2016

Une mise en contexte présente l’évolution de la prise de décision du créateur dans des environnements où la technologie est sans cesse plus impliquée et autonome. Un survol des techniques génératives, de ses origines en musique jusqu’aux arts numériques, est présenté. Trois œuvres réalisées dans le cadre de ce projet de recherche-création sont ensuite analysées. Les outils et les techniques utilisés pour la réalisation de ces projets à dominante générative sont expliqués et commentés. Verklärter Rohr fait l’objet d’une analyse plus poussée sur l’aspect comprovisationnel de mon travail. Le développement de mon langage audiovisuel tel qu’il se rapporte au concept de synchrèse sera aussi examiné. L’analyse de Machine In The Shell, sert de terrain d’investigation pour expliciter des stratégies de spatialisation et de techniques d’écriture. L’analyse de Gu(I)t(A)rs développe sur l’utilisation de diverses technologies dont le Leap Motion, la synthèse par modélisation physique et l’intelligence artificielle. / Questions related to the evolution of creative decision-making are discussed, in relation to technology that is becoming more and more involved. An overview of generative techniques, from its origins in music to contemporary digital arts, is then presented. Then, three works produced as part of this research-creation project are analyzed. The different tools and techniques I used in making these works are explained and commented. Verklärter Rohr is the subject of a more in-depth analysis. The comprovisational angle of my work, as well as the development of my audiovisual language as it relates to the concept of synchresis will be discussed. An analysis of Machine In The Shell explain the strategies of spatialization and writing techniques I used. The analysis of Gu(I)t(A)rs expands on the use of various technologies including Leap Motion, physical modeling synthesis and artificial intelligence.